{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/elasticsearch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elasticsearch Embeddings"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "from llama_index.embeddings.elasticsearch import ElasticsearchEmbedding\n",
    "from llama_index.vector_stores import ElasticsearchStore\n",
    "from llama_index import ServiceContext, StorageContext, VectorStoreIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get credentials and create embeddings\n",
    "\n",
    "import os\n",
    "\n",
    "host = os.environ.get(\"ES_HOST\", \"localhost:9200\")\n",
    "username = os.environ.get(\"ES_USERNAME\", \"elastic\")\n",
    "password = os.environ.get(\"ES_PASSWORD\", \"changeme\")\n",
    "index_name = os.environ.get(\"INDEX_NAME\", \"your-index-name\")\n",
    "model_id = os.environ.get(\"MODEL_ID\", \"your-model-id\")\n",
    "\n",
    "\n",
    "embeddings = ElasticsearchEmbedding.from_credentials(\n",
    "    model_id=model_id, es_url=host, es_username=username, es_password=password\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create service context using the embeddings\n",
    "\n",
    "service_context = ServiceContext(embed_model=embeddings, chunk_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# usage with elasticsearch vector store\n",
    "\n",
    "vector_store = ElasticsearchStore(\n",
    "    index_name=index_name, es_url=host, es_user=username, es_password=password\n",
    ")\n",
    "\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "\n",
    "index = VectorStoreIndex.from_vector_store(\n",
    "    vector_store=vector_store,\n",
    "    storage_context=storage_context,\n",
    "    service_context=service_context,\n",
    ")\n",
    "\n",
    "query_engine = index.as_query_engine()\n",
    "\n",
    "\n",
    "response = query_engine.query(\"hello world\")"
   ]
  }
 ],
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